Minimum rank of matrices described by a graph or pattern over the rational, real and complex numbers ∗ Avi Berman Faculty of Mathematics Technion Haifa 32000, Israel berman@techunix.technion.ac.il Shmuel Friedland Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Chicago, IL 60607-7045, USA friedlan@uic.edu Leslie Hogben Department of Mathematics Iowa State University Ames, IA 50011, USA lhogben@iastate.edu Uriel G. Rothblum Faculty of Industrial Engineering and Management Technion Haifa 32000, Israel rothblum@ie.technion.ac.il Bryan Shader Department of Mathematics University of Wyoming Laramie, WY 82071, USA bshader@uwyo.edu Submitted: Apr 18, 2007; Accepted: Dec 22, 2007; Published: Feb 4, 2008 Mathematics Subject Classification: 05C50 Abstract We use a technique based on matroids to construct two nonzero patterns Z 1 and Z 2 such that the minimum rank of matrices described by Z 1 is less over the complex numbers than over the real numbers, and the minimum rank of matrices described by Z 2 is less over the real numbers than over the rational numbers. The latter example provides a counterexample to a conjecture in [AHKLR] about rational realization of minimum rank of sign patterns. Using Z 1 and Z 2 , we construct symmetric patterns, equivalent to graphs G 1 and G 2 , with the analogous minimum rank properties. We also discuss issues of computational complexity related to minimum rank. Keywords: minimum rank, graph, pattern, zero-nonzero pattern, field, matroid, symmetric matrix, matrix, real, rational, complex. ∗ This research began at the American Institute of Mathematics workshop,“Spectra of Families of Matrices described by Graphs, Digraphs, and Sign Patterns,” and the authors thank AIM and NSF for their support. the electronic journal of combinatorics 15 (2008), #R25 1 1 Introduction The (real symmetric) minimum rank problem (for a graph) is to determine the minimum rank among real symmetric matrices whose zero-nonzero pattern of off-diagonal entries is described by a given (simple) graph G. The zero-nonzero pattern described by the graph has tremendous influence on minimum rank. For example, a matrix associated with a path on n vertices (P n ) is a symmetric tridiagonal matrix with nonzero sub- and super- diagonal, and thus has minimum rank n − 1, whereas the complete graph on n vertices (K n ) has minimum rank 1. For a discussion of the background of the minimum rank problem (and an extensive bibliography), see [FH]. Much of the work on the minimum rank problem has focused on real symmetric ma- trices, but symmetric matrices over other fields have also been studied (see [BHL]). While examples of differences in minimum rank over different fields are known, these examples involve fields of different characteristic or size. We use a technique based on matroids to construct two zero-nonzero patterns C S 1 and C S 2 such that the minimum rank of matrices described by C S 1 is less over the complex numbers than over the real numbers 1 , and the minimum rank of matrices described by C S 2 is less over the real numbers than over the rational numbers. The pattern C S 2 immediately provides a counterexample to a conjec- ture in [AHKLR] about rational realization of minimum rank of sign patterns. We then use C S 1 and C S 2 to construct symmetric patterns, equivalent to graphs G 1 and G 2 , with the analogous minimum rank properties. All graphs discussed in this paper are simple, meaning no loops or multiple edges. The order of a graph G, denoted |G|, is the number of vertices of G. For a symmetric n × n matrix A over a field F , the graph of A, denoted G(A), is the graph with vertices {1, . . . , n} and edges {{i, j}| a ij = 0 and i = j}. Note that the diagonal of A is ignored in determining G(A). The set of symmetric matrices of the graph G over the field F is S F G = {A ∈ F n×n : A T = A and G(A) = G}. Since we will need to consider non-symmetric matrices, as well as matrices over the rational and complex numbers, we adopt the perspective that we are finding the minimum of the ranks of the matrices in a given family F of matrices, and define mr(F) = min{rank(A) : A ∈ F}. Note that what we are denoting by mr(S R G ) is commonly denoted by mr(G) in papers that study only the minimum rank of the real symmetric matrices described by a graph, and mr(S F G ) is sometimes denoted by mr(F, G) or mr F (G). Clearly mr(S Q G ) ≥ mr(S R G ) ≥ mr(S C G ), but in all previously known examples, including all graphs having minimum rank less than 3, the minimum rank was the same for all fields of characteristic zero [BHL]. Using the notation just introduced, in Section 3 we show 1 We thank Chris Godsil and Jim Oxley for providing references to relevant papers on matroids. A good general reference on matroids is [O]. the electronic journal of combinatorics 15 (2008), #R25 2 that mr(S R G 1 ) > mr(S C G 1 ) and mr(S Q G 2 ) > mr(S R G 2 ). However, these examples are quite large (the orders are 75 and 181, respectively). First we show that for very small graphs (order ≤ 6), all these minimum ranks are equal. A cut-vertex of a connected graph is a vertex whose deletion disconnects G. In [BFH] it was shown that if G has a cut-vertex, the problem of computing the minimum rank of G can be reduced to computing minimum ranks of certain subgraphs. Specifically, let v be a cut-vertex of G. For i = 1, . . . , h, let W i be the vertices of the ith component of G−v and let G i be the subgraph induced by {v} ∪ W i . Then r v (G) = min   h 1 r v (G i ), 2  , where r v (G) = mr(G) − mr(G − v) is called the rank-spread of G at vertex v. Thus mr(G) = h  1 mr(G i − v) + min  h  1 r v (G i ), 2  . Wayne Barrett has observed that the proof remains valid over any field. Hence we have the following. Observation 1.1. If the minimum rank of H is independent of field for all H such that |H| < |G| and G has a cut-vertex, then the minimum rank of G is independent of field. Throughout this paper. F denotes a field of characteristic 0, and F n denotes the set of n by 1 vectors with entries in F. A graph is 2-connected if its order is at least 3 and it has no cut-vertex. A linear 2-tree is a 2-connected graph G that can be embedded in the plane such that the graph obtained from the dual of G after deleting the vertex corresponding to the infinite face is a path. Equivalently, a linear 2-tree is a “path” of cycles built up one cycle at a time by identifying an edge of a new cycle with an edge (that has a vertex of degree 2) of the most recently added cycle. In [HH] it is established that for a 2-connected graph G, mr(S R G ) = |G| − 2 if and only if G is a linear 2-tree, but the proof is specific to the real numbers. In [JLS], a complete characterization of graphs having minimum rank |G| − 2 over infinite fields is given, and as a consequence it is shown that for any infinite field F, mr(S F G ) = |G| − 2 if and only if G is a linear 2-tree. (Note that in [JLS] what we call a linear 2-tree is called a linear singly edge-articulated cycle graph or LSEAC graph.) Proposition 1.2. Let G be a connected graph such that |G| ≤ 6 and let F be a field of characteristic 0. Then mr(S F G ) = mr(S R G ). In particular, mr(S Q G ) = mr(S R G ) = mr(S C G ) for any graph G such that |G| ≤ 6. Proof. The result is clear if |G| = 1, 2. In general, mr(S F G ) = 1 if and only if G is a complete graph, and mr(S F G ) = |G| − 1 if and only if G is a path. The latter statement is a consequence of Fiedler’s Tridiagonal Matrix Theorem (proved over the real numbers in [F]; the proof in [RS] is valid for any field of characteristic 0). This establishes the result for |G| = 3, 4. From [BHL], if |G| = 5, mr(S F G ) = 2 if and only if G is not K 5 , not Dart, not , and G does not contain P 4 as an induced subgraph (see Figure 1). For |G| = 5 this is sufficient to establish the result, since for |G| = 5, graphs having minimum rank the electronic journal of combinatorics 15 (2008), #R25 3 3 over F are precisely those not having minimum rank 1, 2, or 4. In [HH] and [JLS] it is shown that for graphs G without cut-vertices, mr(S F G ) = |G| − 2 if and only if G is a linear 2-tree. Together with the fact that the result is true for |G| ≤ 5 and Observation 1.1, this establishes the result for |G| = 6. P 4 Dart  Figure 1: Some forbidden induced subgraphs for mr(S F G ) ≤ 2 Obviously Proposition 1.2 can be applied to conclude that there is no difference in minimum rank over fields of characteristic zero for graphs having each connected compo- nent of order 6 or less, and can be combined with Observation 1.1 to to show that many additional small graphs have no difference in minimum rank over fields of characteristic zero. There is a graph of order 6 for which the minimum rank over Z 2 differs from the minimum rank over R. Example 1.3. Let K 3 K 2 be the graph constructed from two copies of K 3 joined by a complete matching; K 3 K 2 is shown in Figure 2. Then mr(S R K 3 K 2 ) = 3 since K 3 K 2 has an induced P 4 but is not a linear 2-tree (in fact, the block matrix  J − I I I (J − I) −1  , where I is the identity matrix and J is the all ones matrix, has rank 3). Figure 2: The graph K 3 K 2 With appropriate ordering of the vertices, any matrix in S Z 2 (K 3 K 2 ) is of the form         d 1 1 1 1 0 0 1 d 2 1 0 1 0 1 1 d 3 0 0 1 1 0 0 d 4 1 1 0 1 0 1 d 5 1 0 0 1 1 1 d 6         the electronic journal of combinatorics 15 (2008), #R25 4 and computation using all 64 possible (d 1 , . . . , d 6 ) shows the rank is at least 4. In order to construct our examples of graphs where the minimum rank differs over R and C or over R and Q, we will first need to construct examples over non-symmetric nonzero patterns. A nonzero pattern Z = [z ij ] is a matrix whose entries z ij are elements of {∗, 0}. Given a pattern Z = [z ij ], we let M F Z denote the set of all matrices A = [a ij ] over F such that a ij = 0 if and only if z ij = ∗. A realization of Z over F is a matrix in M F Z . Note that (unlike the set of symmetric matrices described by a graph), here the diagonal is constrained by the zero-nonzero pattern. 2 Minimum ranks of patterns over the rational, real and complex numbers Let V be an n by k matrix over F. We denote the nullspace of V , {w ∈ F k : V w = 0}, by NS(V ), and the left nullspace of V , {w ∈ F n : w T V = 0}, by LNS(V ). Throughout most of this section, the of rank of V will be k; in this case, dim(LNS(V )) = n−rank V = n−k. For an m by n matrix A over F, we denote the row space of A (the subspace of F n spanned by the rows of A) by row(A). A cycle of V is a subset α of {1, 2, . . . , n} such that the rows of V indexed by α are linearly dependent and each proper subcollection of these columns is linearly independent. Let α denote the 1 by n pattern obtained from α by placing a ∗ in position j when j ∈ α, and a 0 in position j otherwise. A cycle matrix C V of V is a matrix whose rows are the patterns α as α runs over the cycles of V . Note that we don’t prescribe the ordering of the rows of C V . Thus V has many cycle matrices, but they are all obtained from a single cycle matrix by permutation of rows. Lemma 2.1. Let V be an n by k matrix of rank k with entries from F, and let C V be a cycle matrix of V . Also, let α be the set of indices of a collection of linearly independent rows of V . Then there exists a subset β of row indices and a subset γ of column indices such that α ∩ γ = ∅ and C V [β, γ] is an (n − k) by (n − k) matrix whose rows can be permuted to the matrix        ∗ 0 0 · · · 0 0 ∗ 0 · · · 0 0 0 ∗ . . . 0 . . . . . . . . . . . . . . . 0 0 · · · 0 ∗        Proof. Since V has rank k, we may assume without loss of generality that α is {1, 2, . . . , k}. For each j ∈ {k + 1, . . . , n}, rows 1, 2, . . . , k, j of V are linearly dependent, and thus there is a cycle of V containing j and contained in {1, 2, . . . , k, j}. Hence, there is a row of C V with a ∗ in column j, and 0s in all positions  with  > k and  = j. The result now follows. the electronic journal of combinatorics 15 (2008), #R25 5 Lemma 2.2. Let V be an n by k matrix of rank k with entries from the field F, and let C V be a cycle matrix of V . Then mr(M F C V ) = n − k. Proof. By Lemma 2.1, mr(M F C V ) ≥ n − k. For each row α of C V there is a realization of α that belongs to LNS(V ). Hence, there is a realization A ∈ M F C V such that AV = O. Thus, mr(M F C V ) ≤ rank(A) ≤ n − rank(V ) = n − k. In his early work on matroids [M], Saunders MacLane gave examples of matroids that can be represented over the complex number but not the real numbers and over the real numbers but not the rational numbers. We use these ideas to construct two matrices, and from these matrices, patterns that have differing minimum ranks. We begin with the example that distinguishes the complex numbers from the real numbers. Let S 1 =               1 0 0 0 1 0 1 1 0 1 ω + 1 ω 0 0 1 1 ω + 1 ω + 1 1 1 ω + 1 0 1 1 1 0 ω               where ω = −1+ √ 3i 2 . It is not difficult to verify that the cycles of S 1 correspond to the lines and 4-sets of points in general position of AG(2, 3), the affine plane of order 3, as labeled in Figure 3. There are 12 3-cycles (see Figure 3). Since there are  9 4  4-element subsets, and each 3-cycle excludes 6 of these, there are 126 − (6)(12) = 54 4-cycles and thus a total of 66 cycles of S 1 . Figure 3: Diagram of AG(2, 3) for S 1 We shall make use of several known results, which are a matrix theoretic restatement of MacLane’s results on matroids. the electronic journal of combinatorics 15 (2008), #R25 6 Theorem 2.3. There is no real matrix T such that C T = C S 1 . Proof. Suppose to the contrary that there exists a 9 by  real matrix W = [w ij ] of rank  whose cycle matrix is C S 1 . Since every cycle of S 1 has at least 3 elements, each pair of rows of W are linearly independent. Since every set of 4 rows of S 1 is linearly dependent, so is every set of 4 rows of W . Hence W has rank at most 3 and  ≤ 3. Rows 1, 2 and 5 of S 1 are linearly independent. Thus no cycle of S 1 (and hence of W ) is contained in {1, 2, 5}. Therefore, rows 1, 2, 5 of W are linearly independent. Therefore, W has rank 3, that is,  = 3. Note that post-multiplying W by an invertible (real) matrix, or pre-multiplying W by an invertible (real) diagonal matrix does not change its cycle matrix. Thus, we may assume without loss of generality that the leftmost nonzero entry in each row of W is a 1 and that W [{1, 2, 5}, :] = I 3 . Because {1, 2, 3} is a cycle, and each pair of columns of W is linearly independent, we have that w 31 = 0, w 32 = 0 and w 33 = 0. Thus, by scaling columns and then rows, we may assume without loss of generality that W [{1, 2, 3, 5}, :] =     1 0 0 0 1 0 1 1 0 0 0 1     . Similarly, using that {2, 5, 8} is a cycle of S 1 , we conclude that without loss of generality row 8 of W is  0 1 1  . Using that {1, 5, 9} is a cycle, we see that row 9 of W is  1 0 a  for some nonzero real number a. Next use that {3, 5, 7} is a cycle to conclude that row 7 of W is  1 1 b  for some nonzero real number b. Next use that {1, 6, 8} is a cycle to conclude that row 6 of W has the form  1 c c  for some nonzero real number c. the electronic journal of combinatorics 15 (2008), #R25 7 Thus, we have that W has the form               1 0 0 0 1 0 1 1 0 x y z 0 0 1 1 c c 1 1 b 0 1 1 1 0 a               for some nonzero real numbers, a, b, c and real numbers x, y, z. Since {7, 8, 9} is a cycle, 0 = det   1 1 b 0 1 1 1 0 a   = a + 1 − b. Since {3, 6, 9} is a cycle, 0 = det   1 1 0 1 c c 1 0 a   = ac + c − a. Since {2, 6, 7} is a cycle, 0 = det   0 1 0 1 c c 1 1 b   = c − b. These equations lead to b = a + 1, ac + c − a = 0, and c = b. Thus, c = a + 1, and substitution into the second equation gives: a 2 + a + 1 = 0. Therefore, a = −1± √ −3 2 , which contradicts the fact that W is a real matrix. Therefore, there is no real matrix whose cycle matrix is C S 1 . Corollary 2.4. mr(M R C S 1 ) = 7 > 6 = mr(M C C S 1 ). Proof. By Lemma 2.2, mr(M C C S 1 ) = 6. Let A be a real realization of C S 1 of minimum rank. We claim that rank(A) ≥ 7. Suppose to the contrary that rank(A) ≤ 6. Let W be a real matrix whose columns form a basis for the nullspace of A. By Lemma 2.1, C S 1 contains a submatrix that is a 6 by 6 permutation matrix. Thus, rank(A) = 6 (and so W has 3 columns). Note that since dim row(A) = rank(A) = 6 = 9 − rank(W ), row(A) = LNS(W ) Let α be a collection of row indices such that the set of rows of S 1 indexed by α is linearly independent. By Lemma 2.1, 6 ≤ rank(A[:, α]). The existence of a nonzero vector the electronic journal of combinatorics 15 (2008), #R25 8 v ∈ row(A) whose support is contained in α leads to the contradiction 6 = rank(A) ≥ 1 + rank(A[:, α]) ≥ 1+ 6 = 7. Thus, the row space of A contains no nonzero vector whose support is contained in α. Since row(A) = LNS(W), the set of rows of W indexed by α is linearly independent. We have shown: whenever a collection of rows of S 1 is linearly independent, the corresponding collection of rows of W is also linearly independent (or equivalently, if a collection of rows of W is linearly dependent, then the corresponding collection of rows of S 1 is also linearly dependent). In particular, no pair of rows of W is linearly dependent. Let α be a cycle of W of size 3. Then by the preceding observation the rows of S 1 indexed by α are linearly dependent, and since each pair of rows of S 1 is linearly independent, α is a cycle of S 1 of size 3. Let β be a cycle of S 1 of size 3. Then A contains a nonzero row whose support is β, and hence the rows of W indexed by β are linearly dependent. Since each pair of rows of W is linearly independent, β is a cycle of W of size 3. We have shown that V and W have the same cycles of size 3. The cycles of W (respectively, S 1 ) of size 4 are precisely the 4-sets which contain no cycle of size 3. Thus, the cycles of W and S 1 of size 4 are equal. Since both W and S 1 have rank 3, it follows that W and S 1 have the same cycles. This contradicts Theorem 2.3. Therefore, mr(M R C S 1 ) ≥ 7 > 6 = mr(M C C S 1 ). To see that mr(M R C S 1 ) = 7, consider the 9 by 2 real matrix X whose jth row is [1, j]. Clearly, every 2 by 2 submatrix of X is invertible, and hence for each 1 by 9 pattern with 3 or more nonzeros there is a realization that belongs to the left nullspace of X. Therefore, there is a realization of M R C S 1 of rank at most (and hence exactly) 7. Note that in the proof of Theorem 2.3, no cycle of S 1 containing 4 is used. It follows that there is no real matrix whose cycles are the same as those of S 1 [{4}, :]. As the points of AG(3, 2) are interchangeable, there is no real matrix whose cycles are the same as those of S 1 [{j}, :] for each j. This observation and an argument similar to that of Corollary 2.4 prove the following. Corollary 2.5. Let S be a pattern obtained from S 1 by deleting a row. Then mr(M R C S ) = 6 > 5 = mr(M C C S ). We now construct an example that distinguishes the rational numbers from the real the electronic journal of combinatorics 15 (2008), #R25 9 numbers. Let S 2 =                    1 1 2 + √ 5 2 0 1 1 1 1 − 1 2 + √ 5 2 0 1 0 1 0 1 1 1 1 2 + √ 5 2 1 1 1 3 2 − √ 5 2 1 − 1 2 + √ 5 2 − 1 2 + √ 5 2 1 0 0 0 1 0 0 0 1                    It is not difficult to verify that the 3-cycles of S 2 correspond to the subsets of 3 collinear points in Figure 3 (the details of a computer implementation are given in an appendix, available on line at http://www.aimath.org/∼skrantz/Blurbs/leslie-app.pdf). There are twenty-five 3-cycles, one from each of the five lines with 3 points and four from each of the five lines with 4 points. The 4-cycles are all sets of 4 points that do not contain a 3-cycle. Each line with 3 points excludes eight 4-cycles. Each subset of three points of a line with 4 points excludes seven 4-cycles and the entire line is also excluded, so a line of four points excludes twenty-nine 4-cycles. Thus there are 330 − (8)(5) − (29)(5) = 145 4-cycles, and 170 cycles of S 2 . Figure 4: Diagram for S 2 Theorem 2.6. There is no rational matrix T such that C T = C S 2 . Proof. The proof is much like that of Theorem 2.3, so we only summarize the steps. Suppose to the contrary that W is an 11 by  matrix of rank  over Q whose cycles are those of S 2 . Since each set of 4 rows of S 2 is linearly dependent, and W has the same cycles as S 2 , each set of 4 rows of W is linearly dependent. Thus  ≤ 3. Since {9, 10, 11} contains no cycle of S 2 , rows 9, 10 and 11 of W form a linearly independent set. Hence  = 3. By post-multiplying W by an invertible, rational matrix, without loss of generality, we may assume that W [{9, 10, 11}, :] = I 3 . the electronic journal of combinatorics 15 (2008), #R25 10 [...]... combinatorics 15 (2008), #R25 11 R Counterexample 2.8 Let A be a realization of CS2 of rank 8, and let ZCS2 be the sign pattern of A By Corollary 2.7 there is no rational matrix with sign pattern Z of rank 8 Hence the minimum rank among the rational matrices with sign pattern Z is larger than the minimum rank among the real matrices with sign pattern ZCS2 An explicit example of such ZCS2 and details of. .. proceeds as that of Theorem 3.1 Let A be a matrix whose graph is G2 Thus, A has the form (1) where D and E are diagonal matrices, and B has pattern CS2 We claim that if A is real (respectively rational), then rank A ≥ 16 (respectively, rank A ≥ 18) the electronic journal of combinatorics 15 (2008), #R25 14 As before, the cases E has 0 or at least 16 (or 18 in the rational case) nonzero entries is easily... form D BT B E , (1) where D and E are diagonal matrices, and B has pattern CS1 We claim that if A is complex (respectively real) , then rank( A) ≥ 12 (respectively, rank( A) ≥ 14) the electronic journal of combinatorics 15 (2008), #R25 12 If each diagonal entry of E is 0 and A is complex (respectively, real) , then by Corollary 2.4, rank( A) ≥ rank( B) + rank( B T ) ≥ 6 + 6 = 12 (respectively, rank( A) ≥ rank( B)... Loewy Graphs whose minimal rank is two Electronic Journal of Linear Algebra, 2004, 11:258–280 [BR] A Berman and U Rothblum A note on the computation of the CP -rank, Linear Algebra Appl 2006, 419:1-7 [FH] S.M Fallat and L Hogben The Minimum Rank of Symmetric Matrices Described by a Graph: A Survey Linear Alg Appls., 2007, 426:558–582 [F] M Fiedler A characterization of tridiagonal matrices Linear Alg Appls.,... for the Renegar algorithm are available when executed on parallel processors Some computer algebra systems, such as Mathematica, have implemented quantifier elimination, and Jason Grout [G] has developed a Mathematica notebook to compute the minimum rank of very small graphs over R or C by verifying the validity of statements in (b)-(e) Table 1 lists the values of the corresponding parameters M, d and. .. Is there an upper bound on 5 mr(MRS ) C mr(MCS ) C ≥ 6 5 where CS is the pattern ? mr(MQ ) Z mr(MR ) Z ? Computation of minimum rank The question of the decidability of the minimum rank of a graph over a field F was raised at the 2006 American Institute of Mathematics workshop,“Spectra of Families of Matrices described by Graphs, Digraphs, and Sign Patterns,” and in this section we briefly discuss theoretical... open the electronic journal of combinatorics 15 (2008), #R25 18 References [AHKLR] M Arav, F Hall, S Koyucu, Z Li and B Rao On Rational Realizations of the Minimums Rank of a Sign Pattern Matrix Linear Alg Appls., 2005, 409:11–125 [BFH] F Barioli, S.M Fallat, and L Hogben Computation of minimal rank and path cover number for graphs Linear Alg Appls., 2004, 392:289–303 [BHL] W Barrett, H van der Holst and. .. that mr(SG2 ) = 16 Otherwise, A is rational and rank( A) ≥ 16 + k − 2 k/5 Hence, rank( A) ≥ 18, except possibly in the case that k = 1 This case is handled just as in the proof of Theorem 3.1 Hence, A has rank at least 18, as desired 4 Minimum rank and extension fields Returning now to a not-necessarily symmetric pattern Z with the diagonal restricted by the pattern, it is natural to ask for the relationship... C algorithm for determining mr (G) for any graph G Tarski [T] was the first to observe that quantifier-elimination can also be done over every real closed field; in fact, Tarski produced an algorithm that does it Algorithms have been improved over the years and software for verifying the validity of sentences (that are not too long) over the real or complex numbers is available the electronic journal of. .. Minimum rank calculation using Mathematica Software available from the author (grout@iastate.edu) [HH] L Hogben and H van der Holst Forbidden minors for the class of graphs G with ξ(G) ≤ 2 Linear Alg Appls., 2007, 423: 42–52 [JLS] C R Johnson, R Loewy and P A Smith The graphs for which maximum multiplicity of an Eigenvalue is Two Preprint Available at http://arxiv.org/pdf/math.CO/0701562 [KR] Swastik . minimum rank of a graph over a field F was raised at the 2006 American Institute of Mathematics workshop,“Spectra of Families of Matrices described by Graphs, Digraphs, and Sign Patterns,” and in. rational, complex. ∗ This research began at the American Institute of Mathematics workshop,“Spectra of Families of Matrices described by Graphs, Digraphs, and Sign Patterns,” and the authors thank AIM. Minimum rank of matrices described by a graph or pattern over the rational, real and complex numbers ∗ Avi Berman Faculty of Mathematics Technion Haifa 32000, Israel berman@techunix.technion.ac.il Shmuel